11 citations · 15 across the 15 of their papers we have counts for
16 papers
A Mixture Autoregressive Image Generative Model on Quadtree Regions for Gaussian Noise Removal via Variational Bayes and Gradient Methods
Shota Saito, Yuta Nakahara, Kohei Horinouchi +3
This paper addresses the problem of image denoising for grayscale images. We propose a probabilistic image generative model that combines a quadtree region-partitioning model with…
Variable Splitting Binary Tree Models Based on Bayesian Context Tree Models for Time Series Segmentation
Yuta Nakahara, Shota Saito, Kohei Horinouchi +4
We propose a variable splitting binary tree (VSBT) model based on Bayesian context tree (BCT) models for time series segmentation. Unlike previous applications of BCT models, the t…
Soft Bayesian Context Tree Models for Real-Valued Time Series
Shota Saito, Yuta Nakahara, Toshiyasu Matsushima
This paper proposes the soft Bayesian context tree model (Soft-BCT), which is a novel BCT model for real-valued time series. The Soft-BCT considers soft (probabilistic) splits of t…
A Lower Bound for the Number of Linear Regions of Ternary ReLU Regression Neural Networks
Yuta Nakahara, Manabu Kobayashi, Toshiyasu Matsushima
With the advancement of deep learning, reducing computational complexity and memory consumption has become a critical challenge, and ternary neural networks (NNs) that restrict par…
Variational Bayesian Methods for a Tree-Structured Stick-Breaking Process Mixture of Gaussians by Application of the Bayes Codes for Context Tree Models
Yuta Nakahara
The tree-structured stick-breaking process (TS-SBP) mixture model is a non-parametric Bayesian model that can represent tree-like hierarchical structures among the mixture componen…
An Algorithmic Framework for Constructing Multiple Decision Trees by Evaluating Their Combination Performance Throughout the Construction Process
Keito Tajima, Naoki Ichijo, Yuta Nakahara +1
Predictions using a combination of decision trees are known to be effective in machine learning. Typical ideas for constructing a combination of decision trees for prediction are b…
Boosting-Based Sequential Meta-Tree Ensemble Construction for Improved Decision Trees
Ryota Maniwa, Naoki Ichijo, Yuta Nakahara +1
A decision tree is one of the most popular approaches in machine learning fields. However, it suffers from the problem of overfitting caused by overly deepened trees. Then, a meta-…
Prediction Algorithms Achieving Bayesian Decision Theoretical Optimality Based on Decision Trees as Data Observation Processes
Yuta Nakahara, Shota Saito, Naoki Ichijo +2
In the field of decision trees, most previous studies have difficulty ensuring the statistical optimality of a prediction of new data and suffer from overfitting because trees are…
Batch Updating of a Posterior Tree Distribution over a Meta-Tree
Yuta Nakahara, Toshiyasu Matsushima
Previously, we proposed a probabilistic data generation model represented by an unobservable tree and a sequential updating method to calculate a posterior distribution over a set…
Stochastic 2D Signal Generative Model with Wavelet Packets Basis Regarded as a Random Variable and Bayes Optimal Processing
Ryohei Oka, Yuta Nakahara, Toshiyasu Matsushima
This study deals with two-dimensional (2D) signal processing using the wavelet packet transform. When the basis is unknown the candidate of basis increases in exponential order wit…
Probability Distribution on Rooted Trees
Yuta Nakahara, Shota Saito, Akira Kamatsuka +1
The hierarchical and recursive expressive capability of rooted trees is applicable to represent statistical models in various areas, such as data compression, image processing, and…
Probability Distribution on Full Rooted Trees
Yuta Nakahara, Shota Saito, Akira Kamatsuka +1
The recursive and hierarchical structure of full rooted trees is applicable to represent statistical models in various areas, such as data compression, image processing, and machin…
A Stochastic Model for Block Segmentation of Images Based on the Quadtree and the Bayes Code for It
Yuta Nakahara, Toshiyasu Matsushima
In information theory, lossless compression of general data is based on an explicit assumption of a stochastic generative model on target data. However, in lossless image compressi…
Theoretical Analysis of the Advantage of Deepening Neural Networks
Yasushi Esaki, Yuta Nakahara, Toshiyasu Matsushima
We propose two new criteria to understand the advantage of deepening neural networks. It is important to know the expressivity of functions computable by deep neural networks in or…
Covariance Evolution for Spatially "Mt. Fuji" Coupled LDPC Codes
Yuta Nakahara, Toshiyasu Matsushima
A spatially "Mt. Fuji" coupled low-density parity check (LDPC) ensemble is a modified version of the original spatially coupled (SC) LDPC ensemble. Its desirable properties are fir…
A Novel Scheme to Improve Lossless Image Coders by Explicit Description of Generative Model Classes
Yuta Nakahara, Toshiyasu Matsushima
In this study, we propose a novel scheme for systematic improvement of lossless image compression coders from the point of view of the universal codes in information theory. In the…